Tone detection has a variety of applications in telecommunications systems. Tones can be used for transmitting data, or for signalling purposes for example. They can include pure tones such as fax, modem, dial tone, continuity tone as well as multi-frequency tones such as DTMF (Dual Tone Multi-Frequency), R1 and R2 signalling. In this context, the term "tone" is not intended to encompass tones occurring as part of human speech, for example in tonal languages such as Chinese.
Accurate detection of tones may be critical for maintaining low error rates for data transmission, or for proper operation of switches or other network equipment which relies on signalling tones. Particularly where tones are carried on speech circuits, it can be difficult to distinguish the tones because speech may show the same frequencies for short periods. It can be important to recognise tones quickly, particularly in switches containing echo cancellation circuits that need to be switched off when tones are detected. An example is an adaptive echo canceller, which adapts to speech. Tones in the speech band can affect the operation of such echo cancellation circuits since they will try to alter their coefficients to adapt to the tones. This is undesirable since when speech reoccurs, the canceller will take longer to readapt to the speech, and so echoes may be heard.
Conventional DTMF detection methods use bandpass filter banks and envelope detectors to estimate the level of each of the eight possible frequency components. The frequencies with the highest levels are selected as candidates for DTMF signal. Further processing is required to discriminate real DTMF tones from voice signals or other energy in the voice band. An example is shown in proceedings of the IEEE-SP International Symposium 1994 `Detection of multi-tone signals based on energy operators`, Edgar F. Velez.
It is also known to use digital signal processors (DSP) to perform evaluation of the discrete Fourier transform (DFT) of the signal using algorithms such as the Goertzel algorithm. Conventional methods may be insufficiently reliable, or take too long, or use too much computational resource.
In many cases, there is a limited amount of computing resource available to carry out tone detection operations, particularly in switches where many signals or channels are handled simultaneously. In such cases, reductions in processing requirements per channel can enable greater channel density, which may be commercially very valuable.
In the book "digital processing of speech signals" by Raminer & Schafer published in 1978, ISBN 0-13-213603-1, there is discussion of pitch period detection at Pages 314-319, 372-379 and 150-158. It is used for estimating fundamental frequency in voice signals for speech recognition, to determine whether the speech is voiced or unvoiced, and to enable the speech to be compared to models. At Page 135 it is indicated that pitch period detectors are used in vocoders, in speaker identification and verification systems, and as aids to the handicapped.
Various ways of determining pitch period are known, including an impulse train algorithm shown at Page 136, which is very computationally intensive, a Fourier representation technique shown at Page 314 onwards, and an auto correlation function approach using centre clipping, shown at Pages 150-158.
It is known from U.S. Pat. No. 5,678,221 to detect and use pitch period of a signal to replace a noisy portion of a voice signal with a stored section of the signal before the noise, the stored section being repeated at pitch period intervals. However, there is no suggestion of using pitch period to detect tones as distinct from speech.